Continual Learning in AI Agents - The industry recognizes the gap between AI agents and human learning capabilities, emphasizing the need for agents to continually learn and improve over time [1] - The industry is exploring different methods for AI systems to learn, including weight updates and learning in context using large language models (LLMs) [2] - Reflection over trajectories is emerging as a key theme, allowing agents to update memories, core instructions, and learn new skills [3][4][5] Skill Learning and Implementation - Skill learning involves reflecting over trajectories to learn skills, exemplified by the skill creator skill adapted from Anthropic [8][9] - Deep agent CLI allows specifying environment variables for logging traces, which is useful for reflection [10][11] - The industry is using Langsmith Fetch to grab recent threads from deep agents for reflection and persistent skill creation [12][13] - A practical example demonstrates how an agent can read a JSON file, reflect on its contents, and create a new deep agent skill, showcasing the utility of continual learning [15][16][17] Benefits and Future Directions - Skill learning enables agents to encapsulate standard operating procedures, such as grabbing Langsmith traces, for repeated use [19][20] - Continual learning loop involves agents reflecting on past trajectories to learn facts, memories, skills, and improve instructions [21][22]

Product Overview - Langchain introduces a no-code agent builder, aiming to empower non-technical users to create agents easily [2][4] - The agent builder is built upon the "deep agents" architecture, simplifying agent creation to a configuration of tools and prompts [5][11] - The platform supports both chat-based interaction and autonomous background operation via triggers [27] Key Features and Technologies - Deep agents architecture utilizes sub-agents for handling long-running or context-intensive tasks, improving efficiency [5][35] - The platform incorporates a natural language interface for agent creation, abstracting away the complexities of prompt engineering [14][50] - Human-in-the-loop controls, such as interrupts, allow users to review and approve actions before execution, balancing autonomy with oversight [39][40] User Experience and Iteration - The platform provides a chat interface for testing and iterating on agents, allowing users to understand agent behavior and refine instructions [17][18] - An agent inbox facilitates the management of agent conversations and interrupted actions, mirroring a familiar email experience [41][42] - The platform allows users to iterate on agents by updating the agent over time [17] Integration and Deployment - Agents built in the agent builder are compatible with Langraph, enabling seamless transition to production deployments [45] - The platform currently hosts deep agents in the cloud, with plans to allow users to bring their own deep agents and graph architectures [46][47] Future Development and Feedback - Langchain seeks user feedback on optimizing agent improvement workflows, exploring various methods such as chatbot agents, canvas experiences, and thumbs up/down feedback [56][57] - The company is interested in user input on desired tools and triggers, as well as the experience for core platform teams to add new modules [55]